{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/approximate-knowledge-compilation-by-online","title":"Approximate Knowledge Compilation by Online Collapsed Importance Sampling","arxiv_id":"1805.12565","date":"2018-05-31","proceeding":"NeurIPS 2018 12","authors":["Tal Friedman","Guy Van Den Broeck"],"abstract":"We introduce collapsed compilation, a novel approximate inference algorithm\nfor discrete probabilistic graphical models. It is a collapsed sampling\nalgorithm that incrementally selects which variable to sample next based on the\npartial sample obtained so far. This online collapsing, together with knowledge\ncompilation inference on the remaining variables, naturally exploits local\nstructure and context- specific independence in the distribution. These\nproperties are naturally exploited in exact inference, but are difficult to\nharness for approximate inference. More- over, by having a partially compiled\ncircuit available during sampling, collapsed compilation has access to a highly\neffective proposal distribution for importance sampling. Our experimental\nevaluation shows that collapsed compilation performs well on standard\nbenchmarks. In particular, when the amount of exact inference is equally\nlimited, collapsed compilation is competitive with the state of the art, and\noutperforms it on several benchmarks.","url_abs":"http://arxiv.org/abs/1805.12565v1","url_pdf":"http://arxiv.org/pdf/1805.12565v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"approximate-knowledge-compilation-by-online","repo_url":"https://github.com/UCLA-StarAI/Collapsed-Compilation","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}